Towards Diversified IoT Image Recognition Services in Mobile Edge Computing

With the rapid development of the Internet of Things (IoT) and emerging Mobile Edge Computing (MEC) technologies, various IoT image recognition services are revolutionizing our lives by providing diverse cognitive assistance. However, most existing related approaches are difficult to meet the diversified needs of users because they believe that the MEC platform is a single layer. In addition, due to the mutual interference between the data, it is not easy for them to extract the discriminative features (DFs) necessary to analyze the input data. To this end, this article proposes an IoT image recognition services framework for different needs in the MEC environment, which consists of Hierarchical Discriminative Feature Extraction (HDFE) and Sub-extractor Deployment (Sub-ED) algorithms. We first propose HDFE, which can avoid mutual interference between data by separately optimizing the data structure, thereby generating an extractor that extracts effective DFs. Then there is Sub-ED, which divides the extractor into a series of sub-extractors and deploys them on appropriate MEC platforms. By doing so, the IoT device can connect to the corresponding MEC platform according to its service types, and use the sub-extract to extract DFs. Then, the MEC platform uploads the extracted feature data to the cloud server for further processing, e.g., feature matching. Finally, the cloud server sends the processed result back to the IoT device. Experimental results show that compared with the state-of-the-art approaches, the proposed framework improves recognition accuracy by about 6% and reduces network traffic by up to 94%.

Automated summary

This article proposes an IoT image recognition services framework for different needs in the MEC environment, which improves recognition accuracy by about 6% and reduces network traffic by up to 94% compared to the state-of-the-art approaches.